This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. This is an added subproject to Imaging Core, Project 1 Overall Goal: Currently, most brain image analyses, particularly in the study of neurodegenerative diseases concentrate on a single imaging modality, e.g. structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), perfusion MRI, positron emission tomography (PET), functional MRI. Different imaging modalities provide complementary, but not necessarily independent, information about the brain. New insights may be obtained by performing an integrated analysis of several modalities simultaneously. Such analysis not only enables the discovery of relationships between the modalities, but also allows the discovery of neurodegenerative effects in either one or perhaps several modalities simultaneously.The goal of this project is to develop a general statistical methodology that can be used to analyze several imaging modalities simultaneously in order to increase the statistical power of finding localized characteristics of disease, as well as revealing relationships between the modalities and between different locations in the brain. For this purpose, we assume that the imaging data is given as a set of co-registered scalar images from a number of subjects and corresponding to various imaging modalities. These images may be, among others, volume expansion/contraction obtained from TBM applied to sMRI, blood flow measurements obtained from perfusion MRI, and scalar summaries such as fractional anisotropy obtained from DTI. The specific aims of this subproject are the following. Aim 1: Develop a multivariate statistical methodology for testing the effect of disease status on multimodality imaging simultaneously at each voxel. This includes: a) Comparison of univariate and multivariate regression approaches b) Performance evaluation via simulations Aim 2: Develop a multivariate statistical methodology for testing the effect of disease status of multimodality imaging simultaneously at different voxels. This includes: a) Comparison of cross-correlation analysis and canonical correlation analysis b) Performance evaluation via simulations Aim 3: Implementation of the above methods in the R software and apply them to the ADNI data base.